Delhi Technological University

About: Delhi Technological University is a education organization based out in New Delhi, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 4427 authors who have published 6761 publications receiving 71035 citations. The organization is also known as: Delhi College of Engineering & DTU.

Effect of Particle Size on Shear Stress of Magnetorheological Fluids

Abstract: Magnetorheological fluids (MRF), known for their variable shear stress contain magnetisable micrometer-sized particles (few micrometer to 200 micrometers) in a nonmagnetic carrier liquid To avoid settling of particles, smaller sized (3-10 micrometers) particles are preferred, while larger sized particles can be used in MR brakes, MR clutches, etc as mechanical stirring action in those mechanisms does not allow particles to settle down Ideally larger sized particles provide higher shear stress compared to smaller sized particles However there is need to explore the effect of particle sizes on the shear stress In the current paper, a comparison of different particle sizes on MR effect has been presented Particle size distributions of iron particles were measured using HORIBA Laser Scattering Particle Size Distribution Analyser The particle size distribution, mean sizes and standard deviations have been presented The nature of particle shapes has been observed using scanning electron microscopy To explore the effect of particle sizes, nine MR fluids containing small, large and mixed sized carbonyl iron particles have been synthesized Three concentrations (9%, 18% and 36% by volume) for each size of particles have been used The shear stresses of those MRF samples have been measured using ANTON PAAR MCR-102 Rheometer With increase in volume fraction of iron particles, the MR fluids synthesized using “mixed sized particles” show better shear stress compared to the MR fluids containing “smaller sized spherical shaped particles” and “larger sized flaked shaped particles” at higher shear rate

#suicidal - A Multipronged Approach to Identify and Explore Suicidal Ideation in Twitter

Abstract: Technological advancements have led to the creation of social media platforms like Twitter, where people have started voicing their views over rarely discussed and socially stigmatizing issues. Twitter, is increasingly being used for studying psycho-linguistic phenomenon spanning from expressions of adverse drug reactions, depressions, to suicidality. In this work we focus on identifying suicidal posts from Twitter. Towards this objective we take a multipronged approach and implement different neural network models such assequential models andgraph convolutional networks, that are trained on textual content shared in Twitter, the historical tweeting activity of the users and social network formed between different users posting about suicidality. We train a stacked ensemble of classifiers representing different aspects of suicidal tweeting activity, and achieve state-of-the-art results on a new manually annotated dataset developed by us, that contains textual as well as network information of suicidal tweets. We further investigate into the trained models and perform qualitative analysis showing how historical tweeting activity and rich information embedded in the homophily networks amongst users in Twitter, aids in accurately identifying tweets expressing suicidal intent.

A Parallel Military-Dog-Based Algorithm for Clustering Big Data in Cognitive Industrial Internet of Things

Abstract: With the advancement of wireless communication, Internet of Things (IoT), and big data, high performance data analytic tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big-data-based problems, since it does not require labeled datasets. Recently, metaheuristic algorithms have been efficiently used to solve various clustering problems. However, to handle big datasets produced from IoT devices, these algorithm fail to respond within the desired time due to high computation cost. This article presents a new metaheuristic-based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big datasets. The optimization efficacy the proposed method is validated against 17 benchmark functions, and the results are compared with five other recent algorithms, namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Furthermore, a parallel version of the proposed method is introduced using MapReduce [MapReduce-based MDBO (MR-MDBO)] for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on two benchmark UCI datasets and three real IoT-based datasets produced from industry. The F-measure and computation time of the MR-MDBO is compared with the six other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO-based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times.